{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TorchDyn Quickstart\n",
    "\n",
    "**TorchDyn is the toolkit for continuous models in PyTorch. Play with state-of-the-art architectures or use its powerful libraries to create your own.**\n",
    "\n",
    "\n",
    "Central to the `torchdyn` approach are continuous neural networks, where *width*, *depth* (or both) are taken to their infinite limit. On the optimization front, we consider continuous \"data-stream\" regimes and gradient flow methods, where the dataset represents a time-evolving signal processed by the neural network to adapt its parameters. \n",
    "\n",
    "By providing a centralized, easy-to-access collection of model templates, tutorial and application notebooks, we hope to speed-up research in this area and ultimately contribute to turning neural differential equations into an effective tool for control, system identification and common machine learning tasks.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchdyn.models import *\n",
    "from torchdyn.datasets import *\n",
    "from torchdyn import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate data from a static toy dataset \n",
    "\n",
    "We’ll be generating data from toy datasets. In torchdyn, we provide a wide range of datasets often use to benchmark and understand Neural ODEs. Here we will use the classic moons dataset and train a Neural ODE for binary classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = ToyDataset()\n",
    "X, yn = d.generate(n_samples=512, noise=1e-1, dataset_type='moons')        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "colors = ['orange', 'blue'] \n",
    "fig = plt.figure(figsize=(3,3))\n",
    "ax = fig.add_subplot(111)\n",
    "for i in range(len(X)):\n",
    "    ax.scatter(X[i,0], X[i,1], s=1, color=colors[yn[i].int()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generated data can be easily loaded in the dataloader with standard `PyTorch` calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.utils.data as data\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "X_train = torch.Tensor(X).to(device)\n",
    "y_train = torch.LongTensor(yn.long()).to(device)\n",
    "train = data.TensorDataset(X_train, y_train)\n",
    "trainloader = data.DataLoader(train, batch_size=len(X), shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We utilize [Pytorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning) to handle training loops, logging and general bookkeeping. This allows `torchdyn` and Neural Differential Equations to have access to modern best practices for training and experiment reproducibility. \n",
    "\n",
    "In particular, we combine modular `torchdyn` models with `LightningModules` via a `Learner` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import pytorch_lightning as pl\n",
    "\n",
    "class Learner(pl.LightningModule):\n",
    "    def __init__(self, model:nn.Module):\n",
    "        super().__init__()\n",
    "        self.model = model\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "    \n",
    "    def training_step(self, batch, batch_idx):\n",
    "        x, y = batch      \n",
    "        y_hat = self.model(x)   \n",
    "        loss = nn.CrossEntropyLoss()(y_hat, y)\n",
    "        logs = {'train_loss': loss}\n",
    "        return {'loss': loss, 'log': logs}   \n",
    "    \n",
    "    def configure_optimizers(self):\n",
    "        return torch.optim.Adam(self.model.parameters(), lr=0.01)\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        return trainloader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a  Neural ODE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analogously to most forward neural models we want to realize a map \n",
    "\n",
    "$$\n",
    "        x \\mapsto \\hat y\n",
    "$$\n",
    "\n",
    "where  $\\hat y$ becomes the best approximation of a true output $y$ given an input $x$.\\\n",
    "In torchdyn you can define very simple Neural ODE models of the form \n",
    "\n",
    "$$ \\left\\{\n",
    "    \\begin{aligned}\n",
    "        \\dot{z}(s) &= f(z(s), \\theta)\\\\\n",
    "        z(0) &= x\\\\\n",
    "        \\hat y & = h(1)\n",
    "    \\end{aligned}\n",
    "    \\right. \\quad s\\in[0,1]\n",
    "$$\n",
    "\n",
    "by just specifying a neural network $f$ and giving some simple settings.\n",
    "\n",
    "**Note:** This Neural ODE model is of *depth-invariant* type as neither $f$ explicitly depend on $s$ nor the parameters $\\theta$ are depth-varying. Together with their *depth-variant* counterpart with $s$ concatenated in the vector field was first proposed and implemented by [[Chen T. Q. et al, 2018]](https://arxiv.org/abs/1806.07366)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the vector field (DEFunc) \n",
    "\n",
    "The first step is to define any PyTorch `torch.nn.Module`. This takes the role of the Neural ODE vector field $f(h,\\theta)$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = nn.Sequential(\n",
    "        nn.Linear(2, 64),\n",
    "        nn.Tanh(),\n",
    "        nn.Linear(64, 2)                       \n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case we chose $f$ to be a simple MLP with one hidden layer and $\\tanh$ activation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the NeuralDE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final step to define a Neural ODE is to instantiate the torchdyn's class `NeuralDE` passing some customization arguments and `f` itself.\n",
    "\n",
    "In this case we specify:\n",
    "* we compute backward gradients with the `'adjoint'` method.\n",
    "* we will use the `'dopri5'` (Dormand-Prince) ODE solver from `torchdiffeq`;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = NeuralDE(f, sensitivity='adjoint', solver='dopri5').to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = Learner(model)\n",
    "trainer = pl.Trainer(min_epochs=200, max_epochs=300)\n",
    "trainer.fit(learn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the method `trajectory` of `NeuralDE` objects you can quickly evaluate the entire trajectory of each data point in `X_train` on an interval `s_span`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_span = torch.linspace(0,1,100)\n",
    "trajectory = model.trajectory(X_train, s_span).detach().cpu()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the Training Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can first plot the trajectories of the data points in the depth domain $s$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "color=['orange', 'blue']\n",
    "\n",
    "fig = plt.figure(figsize=(8,2))\n",
    "ax0 = fig.add_subplot(121)\n",
    "ax1 = fig.add_subplot(122)\n",
    "for i in range(500):\n",
    "    ax0.plot(s_span, trajectory[:,i,0], color=color[int(yn[i])], alpha=.1);\n",
    "    ax1.plot(s_span, trajectory[:,i,1], color=color[int(yn[i])], alpha=.1);\n",
    "    \n",
    "ax0.set_xlabel(r\"$s$ [Depth]\")\n",
    "ax0.set_ylabel(r\"$h_0(s)$\")\n",
    "ax0.set_title(\"Dimension 0\")\n",
    "ax1.set_xlabel(r\"$s$ [Depth]\")\n",
    "ax1.set_ylabel(r\"$h_1(s)$\")\n",
    "ax1.set_title(\"Dimension 1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then the trajectory in the *state-space*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(3,3))\n",
    "ax = fig.add_subplot(111)\n",
    "for i in range(500):\n",
    "    ax.plot(trajectory[:,i,0], trajectory[:,i,1], color=color[int(yn[i])], alpha=.1);\n",
    "    \n",
    "ax.set_xlabel(r\"$h_0$\")\n",
    "ax.set_ylabel(r\"$h_1$\")\n",
    "ax.set_title(\"Flows in the state-space\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the Neural ODE steers the data-points into regions of null loss with a continuous flow in the depth domain. Finally, we can also plot the learned vector field $f$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_static_vector_field(model, trajectory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sweet! You trained your first Neural ODE! Now go on and learn more advanced models with the next tutorials**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py37",
   "language": "python",
   "name": "py37"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
